I want to load the output of an asset

my_asset

that materializes something using Dagster maintained S3PickleIOManager (or sometimes filesystem IO manager). I want to do this inside the same codebase that defines the asset. I want to do this without using a dagster asset/op etc., just in a regular function, which will use the output of

my_asset

as an asset factory (the factory will return a sequence of assets, and no assets, if

my_asset

has not been materialized). I thought of using

build_input_context

and then getting the output from the currently configured IOManager. However, how do I configure

build_input_context

for taking in

my_asset

as input? Assume my asset has name

my_asset_name

, asset key

prefix/my_asset_name

, a dynamic partition.

To load the output of an asset

my_asset

that has been materialized using the S3PickleIOManager or FilesystemIOManager, you would typically use the asset's I/O manager within the Dagster framework. However, if you want to access the materialized data outside of the Dagster asset/op context, you would need to interact with the storage system directly, using the same logic that the I/O manager would use. The documentation does not provide a direct example of using

build_input_context

to load an asset outside of the Dagster execution context. The

build_input_context

function is typically used for testing custom I/O managers and is not intended for loading assets in production code. If you want to load the output of

my_asset

in a regular Python function, you would need to manually replicate the logic that the I/O manager uses to retrieve the data. For example, if

my_asset

is using the S3PickleIOManager, you would need to use the

boto3

library (or another S3 client) to download the pickled object from the appropriate S3 bucket and key, and then unpickle it. Similarly, for the FilesystemIOManager, you would read the file from the local filesystem and unpickle the object. Here's a conceptual example of how you might do this for S3, based on the logic that an S3PickleIOManager would use:

python import boto3 import pickle  def load_my_asset_from_s3(bucket_name, object_key):     s3_client = boto3.client('s3')     response = s3_client.get_object(Bucket=bucket_name, Key=object_key)     body = response['Body'].read()     data = pickle.loads(body)     return data  # Usage bucket_name = 'my-s3-bucket' object_key = 'prefix/my_asset_name' my_asset_data = load_my_asset_from_s3(bucket_name, object_key)

For the filesystem:

python import pickle  def load_my_asset_from_filesystem(file_path):     with open(file_path, 'rb') as f:         data = pickle.load(f)     return data  # Usage file_path = '/path/to/prefix/my_asset_name' my_asset_data = load_my_asset_from_filesystem(file_path)

Please note that this approach requires you to know the exact location (bucket and key for S3, file path for the filesystem) where

my_asset

is stored. This location is determined by the configuration of the I/O manager that was used when the asset was materialized. If you need to handle dynamic partitions, you would also need to incorporate the logic to determine the correct partition key to use when constructing the object key or file path. Keep in mind that this approach bypasses the Dagster framework and its asset management features, so you would be responsible for handling any errors or inconsistencies that may arise from directly accessing the storage backend.

@Scout can I use build_output_context instead?

The

build_output_context

function is designed to help test the

handle_output

method of an

IOManager

by constructing an

OutputContext

object with specified parameters. It is not intended for loading assets or their outputs outside of the Dagster execution context. However, if you are looking to create an

OutputContext

for testing purposes or to simulate the behavior of an

IOManager

outside of a Dagster pipeline, you can use

build_output_context

to construct an

OutputContext

with the necessary parameters that would typically be provided by the Dagster framework during execution. Here is an example of how you might use

build_output_context

based on the documentation:

python from dagster import build_output_context  output_context = build_output_context(     step_key="my_step",     name="my_output",     run_id="example_run_id",     asset_key=AssetKey(["prefix", "my_asset_name"]),     partition_key="my_partition_key" )  # You can then use this context to test your IOManager's handle_output method my_io_manager = MyIOManager() my_io_manager.handle_output(output_context, obj=my_data)

This example creates an

OutputContext

that you can use to test your custom

IOManager

. However, it is important to note that this is a testing utility and should not be used as a means to load data in production code. The

handle_output

method is meant to save or handle the output of a step, not to load or retrieve data. To load data, you would typically use the

load_input

method of an

IOManager

within the context of a Dagster pipeline execution. If you need to load data outside of a Dagster pipeline, you should interact with the storage system directly, as described in the previous response.

How can I create the output context of “my_asset”? To see where the values are saved

Creating an

OutputContext

for an asset like

my_asset

is typically done within the Dagster framework when an asset is being computed and its output is being handled by an

IOManager

. The

OutputContext

provides the

IOManager

with the information it needs to save the output of an asset to the appropriate location. If you want to create an

OutputContext

manually, for example, to test your `IOManager`'s

handle_output

method, you can use the

build_output_context

function provided by Dagster. This function allows you to construct an

OutputContext

with specific parameters. Here's an example of how you might create an

OutputContext

for `my_asset`:

python from dagster import build_output_context, AssetKey  # Example parameters for building the output context asset_key = AssetKey(["prefix", "my_asset_name"]) run_id = "example_run_id"  # Replace with your actual run ID output_name = "result"  # The name of the output, if your asset has named outputs  # Build the output context output_context = build_output_context(     asset_key=asset_key,     run_id=run_id,     output_name=output_name,     # You can include additional parameters as needed, such as:     # - resources     # - config     # - metadata (if your asset produces metadata)     # - etc. )  # Now you can use this context to interact with your IOManager # For example, to see where the values are saved, you might do something like this: io_manager = MyIOManager()  # Replace with your actual IOManager handle_output_method = io_manager.handle_output(output_context, value)

Please note that this is a simplified example, and in a real-world scenario, you would need to provide the actual

IOManager

that you are using, along with any other necessary parameters that your

IOManager

expects. Keep in mind that this approach is primarily for testing purposes. In a production Dagster pipeline, the

OutputContext

is automatically constructed and provided to the

IOManager

by the Dagster framework during execution. If you are trying to determine where `my_asset`'s values are saved during actual pipeline execution, you should look at the configuration for the

IOManager

associated with

my_asset

in your pipeline definition. The

IOManager

configuration will dictate where and how the asset's outputs are stored.

@Scout can you show me how OutputContext is created within dagster framework for the asset my_asset, which you can assume is a single @asset decorated function, so an AssetsDefinition.

Within the Dagster framework, an

OutputContext

is created and managed internally during the execution of an asset. When you define an asset using the

@asset

decorator, Dagster takes care of creating the

OutputContext

and passing it to the appropriate

IOManager

to handle the output of the asset. Here's an example of how you might define a simple asset:

python from dagster import asset  @asset def my_asset():     # Perform some computation     result = compute_something()     return result

When

my_asset

is executed within a Dagster pipeline, Dagster will automatically create an

OutputContext

that includes information such as the asset key, run ID, and any other relevant metadata. This context is then used by the

IOManager

to save the output of

my_asset

to the configured storage location. You don't need to manually create an

OutputContext

when using assets in this way. However, if you want to understand what happens under the hood, here's a conceptual overview of the steps Dagster takes to create an

OutputContext

during asset execution: 1. Dagster starts the execution of the pipeline that includes

my_asset

. 2. When it's time to execute

my_asset

, Dagster prepares the execution context, which includes resources, configuration, and logging. 3. Dagster then invokes

my_asset

, passing in the execution context. 4. Once

my_asset

computes its result, Dagster creates an

OutputContext

that encapsulates information about the output, such as: - The asset key (derived from the name of the asset and any namespace provided by the

@asset

decorator). - The run ID of the current pipeline execution. - The output name, which defaults to "result" for a single output asset. - Any additional metadata or configuration that may be associated with the asset. 5. This

OutputContext

is then passed to the

handle_output

method of the

IOManager

configured for

my_asset

. The

IOManager

uses the context to determine where and how to store the output. The actual creation and management of the

OutputContext

are abstracted away from the user, allowing you to focus on defining the computation and business logic of your assets without worrying about the underlying execution details.

@Scout Can you tell me where an asset with key AssetKey[“prefix”,”my_asset”] and standard output result would be stored in the dagster maintainer s3PickleIOManager, assuming the is configured with: bucket name “dagster” and prefix “io-manager”?

When using the

S3PickleIOManager

provided by Dagster with an asset, the location where the asset's output is stored in S3 is determined by the configuration of the

S3PickleIOManager

and the asset's key. Given the following configuration for the `S3PickleIOManager`: - S3 bucket name: "dagster" - S3 prefix: "io-manager" And an asset with the key

AssetKey(["prefix", "my_asset"])

and a standard output named "result", the output would be stored at the following S3 path:

<s3://dagster/io-manager/prefix/my_asset>

The

S3PickleIOManager

uses the S3 bucket and prefix specified in its configuration, followed by the asset key components, to construct the S3 path where the asset's output will be stored. The final component of the asset key is used as the name of the file, and the preceding components are used as directories under the base S3 prefix. Please note that the actual S3 key might also include additional identifiers or metadata, such as run ID or timestamps, depending on the implementation details of the

S3PickleIOManager

and how it handles versioning or partitioning of assets. However, based on the standard behavior described in the documentation, the path provided above is the expected location for the asset's output in S3. Dagster Docs button I/O managers | Dagster button How to set dynamic prefix to S3PickleIOM... button S3PickleIOManager S3 key formatting button Thank you for your feedback!

@Scout also show me where it would be stored for file system io manager

When using the

FilesystemIOManager

provided by Dagster with an asset, the location where the asset's output is stored on the filesystem is determined by the configuration of the

FilesystemIOManager

and the asset's key. Given the following configuration for the `FilesystemIOManager`: - Base directory: "/my/base/path" (as an example) And an asset with the key

AssetKey(["prefix", "my_asset"])

and a standard output named "result", the output would be stored at the following filesystem path:

/my/base/path/prefix/my_asset

The

FilesystemIOManager

uses the base directory specified in its configuration, followed by the asset key components, to construct the filesystem path where the asset's output will be stored. The final component of the asset key is used as the name of the file or directory, and the preceding components are used as parent directories under the base directory. Please note that the actual filesystem path might also include additional identifiers or metadata, such as run ID or timestamps, depending on the implementation details of the

FilesystemIOManager

and how it handles versioning or partitioning of assets. However, based on the standard behavior described in the documentation, the path provided above is the expected location for the asset's output on the filesystem.

@Scout I want to test input of a downstream asset that would consume the output of ‘my_asset’, using either Filesystem IO manager or s3PickleIOManager, both are dagster maintained. Basically, I want to manually call io_manager.handle_input .handle_output manually and test what it outputs. But I want the output and input to be ‘my_asset’ which returns a Python dictionary of str -> str Write Python functions that would achieve the above tests for real evaluations of my_asset

To test the input and output handling of a downstream asset that consumes the output of

my_asset

using either the

FilesystemIOManager

or

s3PickleIOManager

, you can manually invoke the

handle_output

and

load_input

methods of the respective

IOManager

. Below are Python functions that demonstrate how to perform these tests. First, let's define `my_asset`:

python
def my_asset():
    # This asset returns a Python dictionary of str -> str
    return {"key1": "value1", "key2": "value2"}

Now, let's write a function to test the `FilesystemIOManager`:

python
from dagster import build_output_context, build_input_context, AssetKey, FilesystemIOManager
import os
import pickle

def test_filesystem_io_manager(asset_key, base_dir, value):
    # Create an instance of the FilesystemIOManager
    io_manager = FilesystemIOManager(base_dir=base_dir)

    # Build the output context for 'my_asset'
    output_context = build_output_context(
        asset_key=asset_key,
        run_id="test_run_id"
    )

    # Use the handle_output method to store the value
    io_manager.handle_output(output_context, value)

    # Build the input context for the downstream asset
    input_context = build_input_context(
        upstream_output=output_context,
        resource_config={"base_dir": base_dir}
    )

    # Use the load_input method to retrieve the value
    retrieved_value = io_manager.load_input(input_context)
    return retrieved_value

# Usage
asset_key = AssetKey(["prefix", "my_asset"])
base_dir = "/path/to/storage"
value = my_asset()  # The output of my_asset
retrieved_value = test_filesystem_io_manager(asset_key, base_dir, value)
assert retrieved_value == value

Next, let's write a function to test the `s3PickleIOManager`: ```python from dagster_aws.s3 import S3PickleIOManager from dagster import build_output_context, build_input_context, AssetKey import boto3 import pickle def test_s3_pickle_io_manager(asset_key, bucket, s3_prefix, value): # Create an instance of the S3PickleIOManager s3_resource = boto3.resource('s3') io_manager = S3PickleIOManager(s3_resource=s3_resource, s3_bucket=bucket, s3_prefix=s3_prefix) # Build the output context for 'my_asset' output_context = build_output_context( asset_key=asset_key, run_id="test_run_id" ) # Use the handle_output method to store the value io_manager.handle_output(output_context, value) # Build the input context for the downstream asset input_context = build_input_context( upstream_output=output_context, resource_config={"s3_bucket": bucket, "s3_prefix": s3_prefix} ) # Use the load_input method to retrieve the value retrieved_value = io_manager.load_input(input_context) return retrieved_value # Usage asset

@Scout Make this function generic for any Dagster maintained IOManager, testing the handle input and handle output methods. However the generic function should only be generic to the IOManager. I want it to specifically test the dictionary returned by my_asset.

To create a generic function that tests the

handle_output

and

handle_input

methods for any Dagster-maintained

IOManager

, we can define a function that accepts an

IOManager

instance, the asset key, and any necessary configuration for the

IOManager

. This function will specifically test the dictionary returned by

my_asset

. Here's a generic function that does this:

python from dagster import build_output_context, build_input_context, AssetKey, IOManager  def test_io_manager(io_manager: IOManager, asset_key: AssetKey, io_manager_config: dict, value: dict):     # Build the output context for 'my_asset'     output_context = build_output_context(         asset_key=asset_key,         run_id="test_run_id",         resource_config=io_manager_config     )      # Use the handle_output method to store the value     io_manager.handle_output(output_context, value)      # Build the input context for the downstream asset     input_context = build_input_context(         upstream_output=output_context,         resource_config=io_manager_config     )      # Use the load_input method to retrieve the value     retrieved_value = io_manager.load_input(input_context)     return retrieved_value  # Usage example for FilesystemIOManager from dagster import FilesystemIOManager  asset_key = AssetKey(["prefix", "my_asset"]) base_dir = "/path/to/storage" value = {"key1": "value1", "key2": "value2"}  # The output of my_asset  filesystem_io_manager = FilesystemIOManager(base_dir=base_dir) filesystem_retrieved_value = test_io_manager(filesystem_io_manager, asset_key, {"base_dir": base_dir}, value) assert filesystem_retrieved_value == value  # Usage example for S3PickleIOManager from dagster_aws.s3 import S3PickleIOManager import boto3  bucket = "my-s3-bucket" s3_prefix = "my-s3-prefix" s3_resource = boto3.resource('s3')  s3_pickle_io_manager = S3PickleIOManager(s3_resource=s3_resource, s3_bucket=bucket, s3_prefix=s3_prefix) s3_retrieved_value = test_io_manager(s3_pickle_io_manager, asset_key, {"s3_bucket": bucket, "s3_prefix": s3_prefix}, value) assert s3_retrieved_value == value

This function

test_io_manager

is generic in terms of the

IOManager

used. It takes an

IOManager

instance, the asset key, a configuration dictionary specific to the

IOManager

, and the value to be tested (which is the dictionary returned by

my_asset

). The function then tests the

handle_output

and

load_input

methods by storing and retrieving the value, respectively. The usage examples show how to use this function with both the

FilesystemIOManager

and the

S3PickleIOManager

. You would need to replace the

base_dir

,

bucket

, and

s3_prefix

with the actual values for your environment, and ensure that the necessary libraries and AWS credentials are configured for S3 access.

@Scout does the run_id matter in this test? Let’s say I want to use to actually run the asset and load input and the output

In the context of testing the `IOManager`'s

handle_output

and

load_input

methods, the

run_id

is used to uniquely identify a run within Dagster's execution environment. When you are manually invoking these methods for testing purposes, the

run_id

serves as a placeholder to mimic the behavior that would occur during an actual pipeline run. For the

FilesystemIOManager

, the

run_id

typically does not affect the file path where the output is stored or loaded from, as the file path is usually determined by the asset key and the base directory configured for the

IOManager

. For the

S3PickleIOManager

, the

run_id

may be used as part of the S3 key to store outputs if the

IOManager

is configured to include it. This can be useful for distinguishing between different runs' outputs and for traceability. However, if you are testing the

IOManager

outside of a pipeline run and you are not using

run_id

in the S3 key construction, then the

run_id

would not affect the S3 path. If you want to use the

test_io_manager

function to actually run the asset and load the output as input for another asset, you would typically do this within a Dagster pipeline or job, where the

run_id

is automatically managed by the framework. In that case, the

run_id

is important for tracking the execution and lineage of assets. For testing purposes, you can provide any string as the

run_id

, but it should be consistent between the

handle_output

and

load_input

calls to ensure that the output stored by

handle_output

is the same output retrieved by

load_input

. Here's a simplified version of the

test_io_manager

function without the

run_id

parameter, assuming it's not needed for the file path or S3 key construction:

python
def test_io_manager(io_manager: IOManager, asset_key: AssetKey, io_manager_config: dict, value: dict):
    # Build the output context for 'my_asset'
    output_context = build_output_context(
        asset_key=asset_key,
        resource_config=io_manager_config
    )

    # Use the handle_output method to store the value
    io_manager.handle_output(output_context, value)

    # Build the input context for the downstream asset
    input_context = build_input_context(
        upstream_output=output_context,
        resource_config=io_manager_config
    )

    # Use the load_input method to retrieve the value
    retrieved_value = io_manager.load_input(input_context)
    return retrieved_value

When using this function in a real-world scenario, you would replace the placeholder values with the actual configuration for your

IOManager

and the actual output of

my_asset

. If you are running this as part of a Dagster pipeline, the

run_id

would be automatically provided by the Dagster execution environment. in built-in object IO managers, handle l... button When running a solid selection, enable l... button Dagster Docs button How to use materialize_to_memory with de... button Thank you for your feedback!

@Scout How would I build the output context if the my_asset had a dynamic partition scheme?

When dealing with assets that have a dynamic partition scheme, building the

OutputContext

requires specifying the partition information that corresponds to the dynamic partition being materialized. Dynamic partitions are typically used when the set of partitions is not known ahead of time and can change from run to run. To build an

OutputContext

for an asset with dynamic partitions, you would need to provide the

partition_key

that uniquely identifies the partition you are working with. Here's how you can build the

OutputContext

for an asset with dynamic partitions:

python from dagster import build_output_context, AssetKey  # Example function to build output context for a dynamic partition def build_dynamic_output_context(asset_key, partition_key, io_manager_config):     output_context = build_output_context(         asset_key=asset_key,         partition_key=partition_key,         resource_config=io_manager_config     )     return output_context  # Usage asset_key = AssetKey(["prefix", "my_asset"]) partition_key = "dynamic_partition_123"  # Replace with the actual dynamic partition key io_manager_config = {}  # Replace with the actual IOManager configuration  output_context = build_dynamic_output_context(asset_key, partition_key, io_manager_config)

In this example,

partition_key

is a string that represents the dynamic partition key for the asset. This key should match the partition that was used when the asset was materialized. The

io_manager_config

is a dictionary containing the configuration for the

IOManager

that will be used to handle the asset's output. When you use this

OutputContext

with an `IOManager`'s

handle_output

method, the

IOManager

will store the asset's output in a location that is associated with the specified dynamic partition key. Similarly, when you use the

OutputContext

with the

load_input

method, the

IOManager

will retrieve the asset's output from the location corresponding to the dynamic partition key. Please note that the actual implementation of how dynamic partitions are handled can vary depending on the

IOManager

you are using and the storage backend (e.g., filesystem, S3, etc.). The

IOManager

may use the

partition_key

to construct a unique path or key in the storage backend where the asset's output is stored.

@Scout when I try to run .load_input for Dagster maintained FileSystemIOManager, I get the message that this does not have such a method. Can I do this test on FileSystemIOManager or do I have to use a different version.

As of my knowledge cutoff date in early 2023, the

FileSystemIOManager

in Dagster should have both

handle_output

and

load_input

methods, as these are part of the

IOManager

interface that all I/O managers implement. If you are encountering an issue where the

FileSystemIOManager

does not have a

load_input

method, it's possible that there may be a version mismatch or an unexpected error. Here are a few steps you can take to troubleshoot this issue: 1. Check Dagster Version: Ensure that you are using a version of Dagster where the

FileSystemIOManager

is expected to have the

load_input

method. You can check the version of Dagster you are using with

pip show dagster

. 2. Review Documentation: Consult the Dagster documentation for the version of Dagster you are using to confirm that the

FileSystemIOManager

should have the

load_input

method. 3. Update Dagster: If you are using an older version of Dagster, consider updating to the latest version, as the issue may have been resolved in a more recent release. 4. Check for Custom IOManager: Verify that you are indeed using the Dagster-maintained

FileSystemIOManager

and not a custom

IOManager

that may have been mistakenly named similarly. 5. Check Import Statements: Ensure that you are importing the correct

FileSystemIOManager

from the Dagster package. 6. Review Error Message: Look closely at the error message you are receiving. It may contain additional information that can help you identify the source of the problem. 7. Check for Overrides: If you are using a subclass of

FileSystemIOManager

, ensure that the

load_input

method has not been accidentally overridden or removed. 8. Consult Community Resources: If you are still encountering issues, consider reaching out to the Dagster community through forums, GitHub discussions, or the Dagster Slack channel for additional help. If you are following the documentation and using the correct version of Dagster, you should be able to call the

load_input

method on the

FileSystemIOManager

without any issues. If the problem persists, please provide additional context or error messages, and I can help guide you further based on that information.

@Scout Write a pytest test to build input/output context for my_asset, and pass this into a configured FileSystemIOManager to test its load_input method

To write a pytest test that builds input/output contexts for

my_asset

and tests the

load_input

method of a configured

FileSystemIOManager

, you'll need to follow these steps: 1. Define

my_asset

function that returns a dictionary. 2. Configure a

FileSystemIOManager

with a specific base directory. 3. Use

build_output_context

to create an

OutputContext

for

my_asset

. 4. Use

handle_output

to store the output of

my_asset

. 5. Use

build_input_context

to create an

InputContext

that corresponds to the output of

my_asset

. 6. Use

load_input

to retrieve the stored output. 7. Assert that the retrieved value matches the original value. Here's an example of how you might write this test:

python
import os
import pytest
from dagster import build_output_context, build_input_context, AssetKey, FilesystemIOManager

# Define the asset function
def my_asset():
    return {"key1": "value1", "key2": "value2"}

# Test function for the FileSystemIOManager
def test_filesystem_io_manager(tmp_path):
    # Use pytest's tmp_path fixture to create a temporary directory for the test
    base_dir = str(tmp_path)

    # Create an instance of the FileSystemIOManager with the temporary directory
    io_manager = FilesystemIOManager(base_dir=base_dir)

    # Define the asset key
    asset_key = AssetKey(["prefix", "my_asset"])

    # Build the output context for 'my_asset'
    output_context = build_output_context(
        asset_key=asset_key,
        run_id="test_run_id"
    )

    # Get the value from my_asset
    value = my_asset()

    # Use the handle_output method to store the value
    io_manager.handle_output(output_context, value)

    # Build the input context for the downstream asset
    input_context = build_input_context(
        upstream_output=output_context
    )

    # Use the load_input method to retrieve the value
    retrieved_value = io_manager.load_input(input_context)

    # Assert that the retrieved value matches the original value
    assert retrieved_value == value

# Run the test
pytest.main([__file__])

In this test, we're using the

tmp_path

fixture provided by pytest, which creates a temporary directory that is unique to the test function and will be cleaned up after the test finishes. This ensures that the test does not have side effects and does not interfere with other tests or the local filesystem. The

test_filesystem_io_manager

function performs the steps outlined above and asserts that the value retrieved by the

load_input

method matches the original value returned by

my_asset

. If the assertion passes, the test will succeed; otherwise, it will fail, indicating an issue with the `FileSystemIOManager`'s implementation. To run this test, you can execute the test file using pytest from the command line: ```bash